Bulk RNA-Seq
Bulk RNA-Seq - Image 2
Bulk RNA-Seq - Image 3

Bulk RNA-Seq

$500.00

KIT-011 | BULK RNA-SEQ DATA ANALYSES

Add ons

Extraction and differential analysis for lnRNA, miRNA, circRNA

Product price: $500.00
Total options:
Order total:
SKU: 3 Category:

Description

KIT-011 | BULK RNA-SEQ DATA ANALYSES

RNA sequencing (RNA-seq) tells you which genes are active (expressed) in a cell or tissue, how much they’re expressed, and can reveal new gene versions, mutations, or how gene activity changes in disease, healthy vs. sick cells, or different conditions, providing a snapshot of the active transcriptome. It helps identify biomarkers, understand gene function, map cell types, and discover new disease-causing variants missed by DNA sequencing. 

What to expect:

Methods

Data Input

Raw sequencing data in FASTQ format will be uploaded to the BAINOM-nX cloud platform via a secure interface. Users provided metadata annotations describing sample identifiers, experimental conditions, and relevant clinical attributes. This metadata will be integrated into the analysis pipeline to ensure accurate grouping and downstream interpretation.

Preprocessing and Quality Control

Initial processing included Quality Control (QC) using industry-standard metrics to evaluate read quality, GC content, and sequence duplication rates. Low-quality reads and adapter sequences were removed through adapter trimming. Demultiplexing was performed to separate pooled samples based on unique barcodes, ensuring accurate sample-level analysis.

Read Alignment and Quantification

Cleaned reads were aligned to the reference genome using five complementary mapping algorithms (e.g., STAR, HISAT2, Bowtie2, BWA, and Salmon) to maximize alignment accuracy and minimize bias. Post-alignment, feature quantification was conducted using two independent methods to generate robust gene-level and transcript-level counts. This dual approach ensured consistency across different quantification strategies.

RNA Composition and Functional Analysis

The pipeline estimated the proportion of RNA species (mRNA, rRNA, tRNA, and others) to characterize sample composition. Differential expression analysis was performed using statistical models to identify genes with significant expression changes across experimental conditions. Identified genes were subjected to pathway enrichment analysis to uncover biological processes and signaling pathways impacted by the observed expression patterns. Additionally, alternative splicing analysis was conducted to detect transcript isoform diversity and splicing events.

Output and Reporting

BAINOM-nX generated a comprehensive set of outputs, including:

  • QC Reports summarizing sequencing quality and corrections for outliers and missing values.
  • Processed files in standard formats: SAM, BAM, BED, and BedGraph for compatibility with downstream tools.
  • Genome Browser Tracks for visualization of aligned reads and coverage profiles.
  • Feature Count Tables (raw and normalized) for statistical modeling and interpretation.
  • Descriptive Statistics detailing read count distributions, variance, and normalization factors.

Highlights:

  • Provide FASTQ, get results & processed data files for every step of analysis.
  • Multiple analysis methods used for every step so that the outcomes are statistically robust.
  • Transparent | Before purchasing, check workflow details, contents of the output, & selected sample plots.
  • In addition to user provided data, we also run a positive and negative control so troubleshooting can be done if results are not as expected

 

 

Related products